为了降低分水岭算法的过分割问题，提出一种新改进的基于标记的分水岭图像分割方法。该方法是在分水岭算法的基础上，算法直接应用分水岭在原始梯度图像而并非简化之后的图像进行分割，从而保证没有物体边缘信息的丢失。与此同时，新算法设计一种新的标记提取方法，从梯度的低频成份中提取与物体相关的局部极小值。它们将构成二值标记图像。然后，将提取的标记利用形态学极小值标定技术强制作为原始梯度图像的局部极小值，而屏蔽梯度图像中原有的所有局部极小值。最后，分水岭在经过修改之后的梯度图像上进行图像分割，最终获得较好的图像分割结果。利用本文提出的图像分割算法可以获得较为理想的图像分割结果。通过对不同类型的图像进行试验，证明本文提出的图像分割算法能够获得符合人类视觉特点，具有实际意义而且一致的分割区域，以及较为准确、连续、一个像素大小的物体边界。与其他的分水岭改进方法相比，本文提出的方法要求的计算复杂度较低，具有简单的参数，同时能够更为有效地降低分水岭算法的过分割问题。

This paper suggests an improved marker-based watershed image-segmentation method to reduce the over-segmentation of the watershed algorithm. The new method applies the watershed transform directly on the original gradients image instead of simplified image, so that the loss of boundary information can be avoided. On the other hand, we design a new marker-extracted approach to extract the regional minima related to the objects from the low frequency components of the gradients. These extracted minima constitute the binary marker image. And then the extracted markers are imposed on the original gradients as its minima, while all its intrinsic minima are suppressed. Finally, the watershed algorithm is applied to the modified gradients by the markers to reduce effectively the over-segmentation. Across a variety of image types, it is proven that this new method can obtain meaningful and homogeneous regions with accurate, consecutive and one-pixel wide boundary. Compared with other methods, this system requires fewer computations and simpler parameters and can more efficiently reduce the over-segmentation of the watershed algorithm.